Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations103024
Missing cells471498
Missing cells (%)24.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory71.1 MiB
Average record size in memory723.2 B

Variable types

DateTime2
Text2
Categorical8
Numeric6
Boolean1

Alerts

Booking_Status is highly overall correlated with C_TAT and 9 other fieldsHigh correlation
C_TAT is highly overall correlated with Booking_StatusHigh correlation
Canceled_Rides_by_Customer is highly overall correlated with Booking_Status and 2 other fieldsHigh correlation
Canceled_Rides_by_Driver is highly overall correlated with Booking_Status and 2 other fieldsHigh correlation
Customer_Rating is highly overall correlated with Booking_StatusHigh correlation
Driver_Ratings is highly overall correlated with Booking_Status and 2 other fieldsHigh correlation
Incomplete_Rides is highly overall correlated with Booking_Status and 1 other fieldsHigh correlation
Incomplete_Rides_Reason is highly overall correlated with Booking_Status and 1 other fieldsHigh correlation
Payment_Method is highly overall correlated with Booking_StatusHigh correlation
Ride_Distance is highly overall correlated with Booking_Status and 2 other fieldsHigh correlation
V_TAT is highly overall correlated with Booking_StatusHigh correlation
Incomplete_Rides is highly imbalanced (66.7%) Imbalance
V_TAT has 39057 (37.9%) missing values Missing
C_TAT has 39057 (37.9%) missing values Missing
Canceled_Rides_by_Customer has 92525 (89.8%) missing values Missing
Canceled_Rides_by_Driver has 84590 (82.1%) missing values Missing
Incomplete_Rides has 39057 (37.9%) missing values Missing
Incomplete_Rides_Reason has 99098 (96.2%) missing values Missing
Payment_Method has 39057 (37.9%) missing values Missing
Customer_Rating has 39057 (37.9%) missing values Missing
Booking_ID has unique values Unique
Ride_Distance has 39057 (37.9%) zeros Zeros

Reproduction

Analysis started2025-09-13 12:59:22.315544
Analysis finished2025-09-13 13:00:51.282475
Duration1 minute and 28.97 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Date
Date

Distinct40214
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Memory size805.0 KiB
Minimum2024-01-07 00:00:00
Maximum2024-12-07 23:59:00
Invalid dates0
Invalid dates (%)0.0%
2025-09-13T18:30:51.467378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:51.740941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Time
Date

Distinct1440
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size805.0 KiB
Minimum2025-09-13 00:00:00
Maximum2025-09-13 23:59:00
Invalid dates0
Invalid dates (%)0.0%
2025-09-13T18:30:52.000330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:52.270434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Booking_ID
Text

Unique 

Distinct103024
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-09-13T18:30:52.766598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters1339312
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique103024 ?
Unique (%)100.0%

Sample

1st rowCNR7153255142
2nd rowCNR2940424040
3rd rowCNR2982357879
4th rowCNR2395710036
5th rowCNR1797421769
ValueCountFrequency (%)
cnr7153255142 1
 
< 0.1%
cnr8181602032 1
 
< 0.1%
cnr1797421769 1
 
< 0.1%
cnr8787177882 1
 
< 0.1%
cnr3612067560 1
 
< 0.1%
cnr5374902489 1
 
< 0.1%
cnr5030602354 1
 
< 0.1%
cnr6328453219 1
 
< 0.1%
cnr4787583516 1
 
< 0.1%
cnr7943634301 1
 
< 0.1%
Other values (103014) 103014
> 99.9%
2025-09-13T18:30:53.425898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 104631
 
7.8%
5 104545
 
7.8%
6 104394
 
7.8%
4 104286
 
7.8%
7 104276
 
7.8%
8 104106
 
7.8%
9 103771
 
7.7%
3 103764
 
7.7%
2 103624
 
7.7%
C 103024
 
7.7%
Other values (3) 298891
22.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1030240
76.9%
Uppercase Letter 309072
 
23.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 104631
10.2%
5 104545
10.1%
6 104394
10.1%
4 104286
10.1%
7 104276
10.1%
8 104106
10.1%
9 103771
10.1%
3 103764
10.1%
2 103624
10.1%
0 92843
9.0%
Uppercase Letter
ValueCountFrequency (%)
C 103024
33.3%
N 103024
33.3%
R 103024
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1030240
76.9%
Latin 309072
 
23.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 104631
10.2%
5 104545
10.1%
6 104394
10.1%
4 104286
10.1%
7 104276
10.1%
8 104106
10.1%
9 103771
10.1%
3 103764
10.1%
2 103624
10.1%
0 92843
9.0%
Latin
ValueCountFrequency (%)
C 103024
33.3%
N 103024
33.3%
R 103024
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1339312
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 104631
 
7.8%
5 104545
 
7.8%
6 104394
 
7.8%
4 104286
 
7.8%
7 104276
 
7.8%
8 104106
 
7.8%
9 103771
 
7.7%
3 103764
 
7.7%
2 103624
 
7.7%
C 103024
 
7.7%
Other values (3) 298891
22.3%

Booking_Status
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.9 MiB
Success
63967 
Canceled by Driver
18434 
Canceled by Customer
10499 
Driver Not Found
10124 

Length

Max length20
Median length7
Mean length11.177444
Min length7

Characters and Unicode

Total characters1151545
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCanceled by Driver
2nd rowSuccess
3rd rowSuccess
4th rowCanceled by Customer
5th rowSuccess

Common Values

ValueCountFrequency (%)
Success 63967
62.1%
Canceled by Driver 18434
 
17.9%
Canceled by Customer 10499
 
10.2%
Driver Not Found 10124
 
9.8%

Length

2025-09-13T18:30:53.659609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-13T18:30:53.875260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
success 63967
35.3%
canceled 28933
16.0%
by 28933
16.0%
driver 28558
15.8%
customer 10499
 
5.8%
not 10124
 
5.6%
found 10124
 
5.6%

Most occurring characters

ValueCountFrequency (%)
e 160890
14.0%
c 156867
13.6%
s 138433
12.0%
u 84590
 
7.3%
78114
 
6.8%
r 67615
 
5.9%
S 63967
 
5.6%
C 39432
 
3.4%
n 39057
 
3.4%
d 39057
 
3.4%
Other values (12) 283523
24.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 921226
80.0%
Uppercase Letter 152205
 
13.2%
Space Separator 78114
 
6.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 160890
17.5%
c 156867
17.0%
s 138433
15.0%
u 84590
9.2%
r 67615
7.3%
n 39057
 
4.2%
d 39057
 
4.2%
o 30747
 
3.3%
y 28933
 
3.1%
b 28933
 
3.1%
Other values (6) 146104
15.9%
Uppercase Letter
ValueCountFrequency (%)
S 63967
42.0%
C 39432
25.9%
D 28558
18.8%
N 10124
 
6.7%
F 10124
 
6.7%
Space Separator
ValueCountFrequency (%)
78114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1073431
93.2%
Common 78114
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 160890
15.0%
c 156867
14.6%
s 138433
12.9%
u 84590
 
7.9%
r 67615
 
6.3%
S 63967
 
6.0%
C 39432
 
3.7%
n 39057
 
3.6%
d 39057
 
3.6%
o 30747
 
2.9%
Other values (11) 252776
23.5%
Common
ValueCountFrequency (%)
78114
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1151545
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 160890
14.0%
c 156867
13.6%
s 138433
12.0%
u 84590
 
7.3%
78114
 
6.8%
r 67615
 
5.9%
S 63967
 
5.6%
C 39432
 
3.4%
n 39057
 
3.4%
d 39057
 
3.4%
Other values (12) 283523
24.6%
Distinct94544
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2025-09-13T18:30:54.381449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters927216
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86590 ?
Unique (%)84.0%

Sample

1st rowCID713523
2nd rowCID225428
3rd rowCID270156
4th rowCID581320
5th rowCID939555
ValueCountFrequency (%)
cid954071 5
 
< 0.1%
cid966929 4
 
< 0.1%
cid980727 4
 
< 0.1%
cid836942 4
 
< 0.1%
cid189965 4
 
< 0.1%
cid268274 4
 
< 0.1%
cid819034 4
 
< 0.1%
cid952434 4
 
< 0.1%
cid969725 4
 
< 0.1%
cid381415 4
 
< 0.1%
Other values (94534) 102983
> 99.9%
2025-09-13T18:30:55.121502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 103024
11.1%
I 103024
11.1%
D 103024
11.1%
8 63221
 
6.8%
1 63184
 
6.8%
4 63009
 
6.8%
3 62996
 
6.8%
2 62936
 
6.8%
9 62906
 
6.8%
7 62853
 
6.8%
Other values (3) 177039
19.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 618144
66.7%
Uppercase Letter 309072
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 63221
10.2%
1 63184
10.2%
4 63009
10.2%
3 62996
10.2%
2 62936
10.2%
9 62906
10.2%
7 62853
10.2%
6 62816
10.2%
5 62613
10.1%
0 51610
8.3%
Uppercase Letter
ValueCountFrequency (%)
C 103024
33.3%
I 103024
33.3%
D 103024
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 618144
66.7%
Latin 309072
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
8 63221
10.2%
1 63184
10.2%
4 63009
10.2%
3 62996
10.2%
2 62936
10.2%
9 62906
10.2%
7 62853
10.2%
6 62816
10.2%
5 62613
10.1%
0 51610
8.3%
Latin
ValueCountFrequency (%)
C 103024
33.3%
I 103024
33.3%
D 103024
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 927216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 103024
11.1%
I 103024
11.1%
D 103024
11.1%
8 63221
 
6.8%
1 63184
 
6.8%
4 63009
 
6.8%
3 62996
 
6.8%
2 62936
 
6.8%
9 62906
 
6.8%
7 62853
 
6.8%
Other values (3) 177039
19.1%

Vehicle_Type
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
Prime Sedan
14877 
eBike
14816 
Auto
14755 
Prime Plus
14707 
Bike
14662 
Other values (2)
29207 

Length

Max length11
Median length10
Mean length6.7223948
Min length4

Characters and Unicode

Total characters692568
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrime Sedan
2nd rowBike
3rd rowPrime SUV
4th roweBike
5th rowMini

Common Values

ValueCountFrequency (%)
Prime Sedan 14877
14.4%
eBike 14816
14.4%
Auto 14755
14.3%
Prime Plus 14707
14.3%
Bike 14662
14.2%
Prime SUV 14655
14.2%
Mini 14552
14.1%

Length

2025-09-13T18:30:55.381729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-13T18:30:55.690794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
prime 44239
30.0%
sedan 14877
 
10.1%
ebike 14816
 
10.1%
auto 14755
 
10.0%
plus 14707
 
10.0%
bike 14662
 
10.0%
suv 14655
 
10.0%
mini 14552
 
9.9%

Most occurring characters

ValueCountFrequency (%)
e 103410
14.9%
i 102821
14.8%
P 58946
 
8.5%
r 44239
 
6.4%
m 44239
 
6.4%
44239
 
6.4%
S 29532
 
4.3%
k 29478
 
4.3%
B 29478
 
4.3%
u 29462
 
4.3%
Other values (11) 176724
25.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 471756
68.1%
Uppercase Letter 176573
 
25.5%
Space Separator 44239
 
6.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 103410
21.9%
i 102821
21.8%
r 44239
9.4%
m 44239
9.4%
k 29478
 
6.2%
u 29462
 
6.2%
n 29429
 
6.2%
a 14877
 
3.2%
d 14877
 
3.2%
t 14755
 
3.1%
Other values (3) 44169
9.4%
Uppercase Letter
ValueCountFrequency (%)
P 58946
33.4%
S 29532
16.7%
B 29478
16.7%
A 14755
 
8.4%
U 14655
 
8.3%
V 14655
 
8.3%
M 14552
 
8.2%
Space Separator
ValueCountFrequency (%)
44239
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 648329
93.6%
Common 44239
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 103410
16.0%
i 102821
15.9%
P 58946
9.1%
r 44239
 
6.8%
m 44239
 
6.8%
S 29532
 
4.6%
k 29478
 
4.5%
B 29478
 
4.5%
u 29462
 
4.5%
n 29429
 
4.5%
Other values (10) 147295
22.7%
Common
ValueCountFrequency (%)
44239
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 692568
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 103410
14.9%
i 102821
14.8%
P 58946
 
8.5%
r 44239
 
6.4%
m 44239
 
6.4%
44239
 
6.4%
S 29532
 
4.3%
k 29478
 
4.3%
B 29478
 
4.3%
u 29462
 
4.3%
Other values (11) 176724
25.5%

Pickup_Location
Categorical

Distinct50
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
Banashankari
 
2201
Yeshwanthpur
 
2139
RT Nagar
 
2135
Indiranagar
 
2133
Sahakar Nagar
 
2126
Other values (45)
92290 

Length

Max length20
Median length16
Mean length10.525072
Min length6

Characters and Unicode

Total characters1084335
Distinct characters47
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTumkur Road
2nd rowMagadi Road
3rd rowSahakar Nagar
4th rowHSR Layout
5th rowRajajinagar

Common Values

ValueCountFrequency (%)
Banashankari 2201
 
2.1%
Yeshwanthpur 2139
 
2.1%
RT Nagar 2135
 
2.1%
Indiranagar 2133
 
2.1%
Sahakar Nagar 2126
 
2.1%
Basavanagudi 2120
 
2.1%
Ramamurthy Nagar 2116
 
2.1%
Vijayanagar 2113
 
2.1%
Tumkur Road 2105
 
2.0%
Cox Town 2100
 
2.0%
Other values (40) 81736
79.3%

Length

2025-09-13T18:30:55.988550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
road 14527
 
10.1%
nagar 10453
 
7.2%
town 8269
 
5.7%
layout 4133
 
2.9%
banashankari 2201
 
1.5%
yeshwanthpur 2139
 
1.5%
rt 2135
 
1.5%
indiranagar 2133
 
1.5%
sahakar 2126
 
1.5%
basavanagudi 2120
 
1.5%
Other values (46) 94272
65.2%

Most occurring characters

ValueCountFrequency (%)
a 222500
20.5%
r 84641
 
7.8%
n 66068
 
6.1%
i 49361
 
4.6%
o 47493
 
4.4%
h 45263
 
4.2%
e 43176
 
4.0%
41484
 
3.8%
g 41243
 
3.8%
d 35103
 
3.2%
Other values (37) 408003
37.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 881797
81.3%
Uppercase Letter 161054
 
14.9%
Space Separator 41484
 
3.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 222500
25.2%
r 84641
 
9.6%
n 66068
 
7.5%
i 49361
 
5.6%
o 47493
 
5.4%
h 45263
 
5.1%
e 43176
 
4.9%
g 41243
 
4.7%
d 35103
 
4.0%
l 34689
 
3.9%
Other values (15) 212260
24.1%
Uppercase Letter
ValueCountFrequency (%)
R 28909
17.9%
T 14600
9.1%
M 14475
9.0%
N 12536
 
7.8%
B 10466
 
6.5%
H 10294
 
6.4%
K 10276
 
6.4%
S 10267
 
6.4%
P 8170
 
5.1%
C 8119
 
5.0%
Other values (11) 32942
20.5%
Space Separator
ValueCountFrequency (%)
41484
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1042851
96.2%
Common 41484
 
3.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 222500
21.3%
r 84641
 
8.1%
n 66068
 
6.3%
i 49361
 
4.7%
o 47493
 
4.6%
h 45263
 
4.3%
e 43176
 
4.1%
g 41243
 
4.0%
d 35103
 
3.4%
l 34689
 
3.3%
Other values (36) 373314
35.8%
Common
ValueCountFrequency (%)
41484
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1084335
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 222500
20.5%
r 84641
 
7.8%
n 66068
 
6.1%
i 49361
 
4.6%
o 47493
 
4.4%
h 45263
 
4.2%
e 43176
 
4.0%
41484
 
3.8%
g 41243
 
3.8%
d 35103
 
3.2%
Other values (37) 408003
37.6%

Drop_Location
Categorical

Distinct50
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
Peenya
 
2159
Mysore Road
 
2148
MG Road
 
2128
Hennur
 
2120
HSR Layout
 
2117
Other values (45)
92352 

Length

Max length20
Median length16
Mean length10.505067
Min length6

Characters and Unicode

Total characters1082274
Distinct characters47
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRT Nagar
2nd rowVarthur
3rd rowVarthur
4th rowVijayanagar
5th rowChamarajpet

Common Values

ValueCountFrequency (%)
Peenya 2159
 
2.1%
Mysore Road 2148
 
2.1%
MG Road 2128
 
2.1%
Hennur 2120
 
2.1%
HSR Layout 2117
 
2.1%
Sarjapur Road 2108
 
2.0%
Koramangala 2105
 
2.0%
Marathahalli 2104
 
2.0%
Vijayanagar 2103
 
2.0%
Hebbal 2097
 
2.0%
Other values (40) 81835
79.4%

Length

2025-09-13T18:30:56.235402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
road 14661
 
10.2%
nagar 10275
 
7.1%
town 8135
 
5.6%
layout 4213
 
2.9%
peenya 2159
 
1.5%
mysore 2148
 
1.5%
mg 2128
 
1.5%
hennur 2120
 
1.5%
hsr 2117
 
1.5%
sarjapur 2108
 
1.5%
Other values (46) 94306
65.3%

Most occurring characters

ValueCountFrequency (%)
a 222561
20.6%
r 84371
 
7.8%
n 65829
 
6.1%
i 48955
 
4.5%
o 47548
 
4.4%
h 44912
 
4.1%
e 43208
 
4.0%
41346
 
3.8%
g 41190
 
3.8%
d 35003
 
3.2%
Other values (37) 407351
37.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 879779
81.3%
Uppercase Letter 161149
 
14.9%
Space Separator 41346
 
3.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 222561
25.3%
r 84371
 
9.6%
n 65829
 
7.5%
i 48955
 
5.6%
o 47548
 
5.4%
h 44912
 
5.1%
e 43208
 
4.9%
g 41190
 
4.7%
d 35003
 
4.0%
l 34846
 
4.0%
Other values (15) 211356
24.0%
Uppercase Letter
ValueCountFrequency (%)
R 28974
18.0%
M 14650
9.1%
T 14380
8.9%
N 12292
 
7.6%
H 10458
 
6.5%
S 10422
 
6.5%
K 10393
 
6.4%
B 10340
 
6.4%
P 8332
 
5.2%
C 8083
 
5.0%
Other values (11) 32825
20.4%
Space Separator
ValueCountFrequency (%)
41346
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1040928
96.2%
Common 41346
 
3.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 222561
21.4%
r 84371
 
8.1%
n 65829
 
6.3%
i 48955
 
4.7%
o 47548
 
4.6%
h 44912
 
4.3%
e 43208
 
4.2%
g 41190
 
4.0%
d 35003
 
3.4%
l 34846
 
3.3%
Other values (36) 372505
35.8%
Common
ValueCountFrequency (%)
41346
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1082274
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 222561
20.6%
r 84371
 
7.8%
n 65829
 
6.1%
i 48955
 
4.5%
o 47548
 
4.4%
h 44912
 
4.1%
e 43208
 
4.0%
41346
 
3.8%
g 41190
 
3.8%
d 35003
 
3.2%
Other values (37) 407351
37.6%

V_TAT
Real number (ℝ)

High correlation  Missing 

Distinct40
Distinct (%)0.1%
Missing39057
Missing (%)37.9%
Infinite0
Infinite (%)0.0%
Mean170.87695
Minimum35
Maximum308
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.0 KiB
2025-09-13T18:30:56.460855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile49
Q198
median168
Q3238
95-th percentile301
Maximum308
Range273
Interquartile range (IQR)140

Descriptive statistics

Standard deviation80.80364
Coefficient of variation (CV)0.47287617
Kurtosis-1.201942
Mean170.87695
Median Absolute Deviation (MAD)70
Skewness0.013318788
Sum10930486
Variance6529.2282
MonotonicityNot monotonic
2025-09-13T18:30:56.695288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
56 1670
 
1.6%
196 1665
 
1.6%
105 1652
 
1.6%
168 1650
 
1.6%
126 1644
 
1.6%
224 1637
 
1.6%
98 1632
 
1.6%
63 1631
 
1.6%
308 1630
 
1.6%
84 1628
 
1.6%
Other values (30) 47528
46.1%
(Missing) 39057
37.9%
ValueCountFrequency (%)
35 1584
1.5%
42 1606
1.6%
49 1564
1.5%
56 1670
1.6%
63 1631
1.6%
70 1614
1.6%
77 1623
1.6%
84 1628
1.6%
91 1624
1.6%
98 1632
1.6%
ValueCountFrequency (%)
308 1630
1.6%
301 1594
1.5%
294 1555
1.5%
287 1553
1.5%
280 1611
1.6%
273 1564
1.5%
266 1589
1.5%
259 1597
1.6%
252 1571
1.5%
245 1567
1.5%

C_TAT
Real number (ℝ)

High correlation  Missing 

Distinct25
Distinct (%)< 0.1%
Missing39057
Missing (%)37.9%
Infinite0
Infinite (%)0.0%
Mean84.873372
Minimum25
Maximum145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.0 KiB
2025-09-13T18:30:56.943000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile30
Q155
median85
Q3115
95-th percentile140
Maximum145
Range120
Interquartile range (IQR)60

Descriptive statistics

Standard deviation36.0051
Coefficient of variation (CV)0.42422139
Kurtosis-1.2023627
Mean84.873372
Median Absolute Deviation (MAD)30
Skewness0.0072118859
Sum5429095
Variance1296.3672
MonotonicityNot monotonic
2025-09-13T18:30:57.175666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
60 2664
 
2.6%
55 2642
 
2.6%
95 2631
 
2.6%
40 2627
 
2.5%
35 2626
 
2.5%
110 2588
 
2.5%
105 2585
 
2.5%
115 2577
 
2.5%
100 2576
 
2.5%
145 2574
 
2.5%
Other values (15) 37877
36.8%
(Missing) 39057
37.9%
ValueCountFrequency (%)
25 2478
2.4%
30 2560
2.5%
35 2626
2.5%
40 2627
2.5%
45 2525
2.5%
50 2519
2.4%
55 2642
2.6%
60 2664
2.6%
65 2541
2.5%
70 2528
2.5%
ValueCountFrequency (%)
145 2574
2.5%
140 2548
2.5%
135 2513
2.4%
130 2537
2.5%
125 2431
2.4%
120 2565
2.5%
115 2577
2.5%
110 2588
2.5%
105 2585
2.5%
100 2576
2.5%

Canceled_Rides_by_Customer
Categorical

High correlation  Missing 

Distinct5
Distinct (%)< 0.1%
Missing92525
Missing (%)89.8%
Memory size5.7 MiB
Driver is not moving towards pickup location
3175 
Driver asked to cancel
2670 
Change of plans
2081 
AC is Not working
1568 
Wrong Address
1005 

Length

Max length44
Median length22
Mean length25.657301
Min length13

Characters and Unicode

Total characters269376
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDriver is not moving towards pickup location
2nd rowDriver asked to cancel
3rd rowDriver is not moving towards pickup location
4th rowAC is Not working
5th rowDriver asked to cancel

Common Values

ValueCountFrequency (%)
Driver is not moving towards pickup location 3175
 
3.1%
Driver asked to cancel 2670
 
2.6%
Change of plans 2081
 
2.0%
AC is Not working 1568
 
1.5%
Wrong Address 1005
 
1.0%
(Missing) 92525
89.8%

Length

2025-09-13T18:30:57.424892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-13T18:30:57.628262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
driver 5845
12.3%
is 4743
10.0%
not 4743
10.0%
moving 3175
 
6.7%
towards 3175
 
6.7%
pickup 3175
 
6.7%
location 3175
 
6.7%
cancel 2670
 
5.6%
to 2670
 
5.6%
asked 2670
 
5.6%
Other values (7) 11389
24.0%

Most occurring characters

ValueCountFrequency (%)
36931
13.7%
o 24767
 
9.2%
i 21681
 
8.0%
n 18930
 
7.0%
r 18443
 
6.8%
a 15852
 
5.9%
s 14679
 
5.4%
e 14271
 
5.3%
t 13763
 
5.1%
c 11690
 
4.3%
Other values (16) 78369
29.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 217805
80.9%
Space Separator 36931
 
13.7%
Uppercase Letter 14640
 
5.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 24767
11.4%
i 21681
 
10.0%
n 18930
 
8.7%
r 18443
 
8.5%
a 15852
 
7.3%
s 14679
 
6.7%
e 14271
 
6.6%
t 13763
 
6.3%
c 11690
 
5.4%
v 9020
 
4.1%
Other values (10) 54709
25.1%
Uppercase Letter
ValueCountFrequency (%)
D 5845
39.9%
C 3649
24.9%
A 2573
17.6%
N 1568
 
10.7%
W 1005
 
6.9%
Space Separator
ValueCountFrequency (%)
36931
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 232445
86.3%
Common 36931
 
13.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 24767
 
10.7%
i 21681
 
9.3%
n 18930
 
8.1%
r 18443
 
7.9%
a 15852
 
6.8%
s 14679
 
6.3%
e 14271
 
6.1%
t 13763
 
5.9%
c 11690
 
5.0%
v 9020
 
3.9%
Other values (15) 69349
29.8%
Common
ValueCountFrequency (%)
36931
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 269376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
36931
13.7%
o 24767
 
9.2%
i 21681
 
8.0%
n 18930
 
7.0%
r 18443
 
6.8%
a 15852
 
5.9%
s 14679
 
5.4%
e 14271
 
5.3%
t 13763
 
5.1%
c 11690
 
4.3%
Other values (16) 78369
29.1%

Canceled_Rides_by_Driver
Categorical

High correlation  Missing 

Distinct4
Distinct (%)< 0.1%
Missing84590
Missing (%)82.1%
Memory size5.9 MiB
Personal & Car related issue
6542 
Customer related issue
5413 
Customer was coughing/sick
3654 
More than permitted people in there
2825 

Length

Max length35
Median length28
Mean length26.914452
Min length22

Characters and Unicode

Total characters496141
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPersonal & Car related issue
2nd rowPersonal & Car related issue
3rd rowPersonal & Car related issue
4th rowPersonal & Car related issue
5th rowCustomer was coughing/sick

Common Values

ValueCountFrequency (%)
Personal & Car related issue 6542
 
6.3%
Customer related issue 5413
 
5.3%
Customer was coughing/sick 3654
 
3.5%
More than permitted people in there 2825
 
2.7%
(Missing) 84590
82.1%

Length

2025-09-13T18:30:57.891376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-13T18:30:58.117186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
related 11955
15.6%
issue 11955
15.6%
customer 9067
11.8%
personal 6542
8.5%
6542
8.5%
car 6542
8.5%
was 3654
 
4.8%
coughing/sick 3654
 
4.8%
more 2825
 
3.7%
than 2825
 
3.7%
Other values (4) 11300
14.7%

Most occurring characters

ValueCountFrequency (%)
e 71249
14.4%
58427
11.8%
s 46827
 
9.4%
r 42581
 
8.6%
t 32322
 
6.5%
a 31518
 
6.4%
o 24913
 
5.0%
i 24913
 
5.0%
u 24676
 
5.0%
l 21322
 
4.3%
Other values (14) 117393
23.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 402542
81.1%
Space Separator 58427
 
11.8%
Uppercase Letter 24976
 
5.0%
Other Punctuation 10196
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 71249
17.7%
s 46827
11.6%
r 42581
10.6%
t 32322
8.0%
a 31518
7.8%
o 24913
 
6.2%
i 24913
 
6.2%
u 24676
 
6.1%
l 21322
 
5.3%
n 15846
 
3.9%
Other values (8) 66375
16.5%
Uppercase Letter
ValueCountFrequency (%)
C 15609
62.5%
P 6542
26.2%
M 2825
 
11.3%
Other Punctuation
ValueCountFrequency (%)
& 6542
64.2%
/ 3654
35.8%
Space Separator
ValueCountFrequency (%)
58427
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 427518
86.2%
Common 68623
 
13.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 71249
16.7%
s 46827
11.0%
r 42581
10.0%
t 32322
 
7.6%
a 31518
 
7.4%
o 24913
 
5.8%
i 24913
 
5.8%
u 24676
 
5.8%
l 21322
 
5.0%
n 15846
 
3.7%
Other values (11) 91351
21.4%
Common
ValueCountFrequency (%)
58427
85.1%
& 6542
 
9.5%
/ 3654
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 496141
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 71249
14.4%
58427
11.8%
s 46827
 
9.4%
r 42581
 
8.6%
t 32322
 
6.5%
a 31518
 
6.4%
o 24913
 
5.0%
i 24913
 
5.0%
u 24676
 
5.0%
l 21322
 
4.3%
Other values (14) 117393
23.7%

Incomplete_Rides
Boolean

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing39057
Missing (%)37.9%
Memory size201.3 KiB
False
60041 
True
 
3926
(Missing)
39057 
ValueCountFrequency (%)
False 60041
58.3%
True 3926
 
3.8%
(Missing) 39057
37.9%
2025-09-13T18:30:58.335322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Incomplete_Rides_Reason
Categorical

High correlation  Missing 

Distinct3
Distinct (%)0.1%
Missing99098
Missing (%)96.2%
Memory size5.5 MiB
Customer Demand
1601 
Vehicle Breakdown
1591 
Other Issue
734 

Length

Max length17
Median length15
Mean length15.062659
Min length11

Characters and Unicode

Total characters59136
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCustomer Demand
2nd rowVehicle Breakdown
3rd rowCustomer Demand
4th rowOther Issue
5th rowOther Issue

Common Values

ValueCountFrequency (%)
Customer Demand 1601
 
1.6%
Vehicle Breakdown 1591
 
1.5%
Other Issue 734
 
0.7%
(Missing) 99098
96.2%

Length

2025-09-13T18:30:58.535150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-13T18:30:58.766622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
customer 1601
20.4%
demand 1601
20.4%
vehicle 1591
20.3%
breakdown 1591
20.3%
other 734
9.3%
issue 734
9.3%

Most occurring characters

ValueCountFrequency (%)
e 9443
16.0%
r 3926
 
6.6%
3926
 
6.6%
m 3202
 
5.4%
n 3192
 
5.4%
d 3192
 
5.4%
o 3192
 
5.4%
a 3192
 
5.4%
s 3069
 
5.2%
t 2335
 
3.9%
Other values (13) 20467
34.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 47358
80.1%
Uppercase Letter 7852
 
13.3%
Space Separator 3926
 
6.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 9443
19.9%
r 3926
 
8.3%
m 3202
 
6.8%
n 3192
 
6.7%
d 3192
 
6.7%
o 3192
 
6.7%
a 3192
 
6.7%
s 3069
 
6.5%
t 2335
 
4.9%
u 2335
 
4.9%
Other values (6) 10280
21.7%
Uppercase Letter
ValueCountFrequency (%)
C 1601
20.4%
D 1601
20.4%
V 1591
20.3%
B 1591
20.3%
O 734
9.3%
I 734
9.3%
Space Separator
ValueCountFrequency (%)
3926
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 55210
93.4%
Common 3926
 
6.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 9443
17.1%
r 3926
 
7.1%
m 3202
 
5.8%
n 3192
 
5.8%
d 3192
 
5.8%
o 3192
 
5.8%
a 3192
 
5.8%
s 3069
 
5.6%
t 2335
 
4.2%
u 2335
 
4.2%
Other values (12) 18132
32.8%
Common
ValueCountFrequency (%)
3926
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59136
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 9443
16.0%
r 3926
 
6.6%
3926
 
6.6%
m 3202
 
5.4%
n 3192
 
5.4%
d 3192
 
5.4%
o 3192
 
5.4%
a 3192
 
5.4%
s 3069
 
5.2%
t 2335
 
3.9%
Other values (13) 20467
34.6%

Booking_Value
Real number (ℝ)

Distinct2883
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean548.75188
Minimum100
Maximum2999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.0 KiB
2025-09-13T18:30:59.007601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile128
Q1242
median386
Q3621
95-th percentile1899
Maximum2999
Range2899
Interquartile range (IQR)379

Descriptive statistics

Standard deviation536.54122
Coefficient of variation (CV)0.9777483
Kurtosis6.8312418
Mean548.75188
Median Absolute Deviation (MAD)162
Skewness2.5717109
Sum56534614
Variance287876.48
MonotonicityNot monotonic
2025-09-13T18:30:59.527149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
305 226
 
0.2%
459 216
 
0.2%
191 215
 
0.2%
256 214
 
0.2%
470 212
 
0.2%
176 210
 
0.2%
400 210
 
0.2%
493 208
 
0.2%
142 208
 
0.2%
186 207
 
0.2%
Other values (2873) 100898
97.9%
ValueCountFrequency (%)
100 160
0.2%
101 174
0.2%
102 169
0.2%
103 166
0.2%
104 192
0.2%
105 183
0.2%
106 193
0.2%
107 163
0.2%
108 163
0.2%
109 197
0.2%
ValueCountFrequency (%)
2999 3
 
< 0.1%
2998 8
< 0.1%
2997 5
< 0.1%
2996 4
< 0.1%
2995 3
 
< 0.1%
2994 5
< 0.1%
2993 6
< 0.1%
2991 4
< 0.1%
2990 4
< 0.1%
2989 6
< 0.1%

Payment_Method
Categorical

High correlation  Missing 

Distinct4
Distinct (%)< 0.1%
Missing39057
Missing (%)37.9%
Memory size5.3 MiB
Cash
35022 
UPI
25881 
Credit Card
 
2435
Debit Card
 
629

Length

Max length11
Median length4
Mean length3.9208654
Min length3

Characters and Unicode

Total characters250806
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCash
2nd rowUPI
3rd rowCredit Card
4th rowUPI
5th rowCash

Common Values

ValueCountFrequency (%)
Cash 35022
34.0%
UPI 25881
25.1%
Credit Card 2435
 
2.4%
Debit Card 629
 
0.6%
(Missing) 39057
37.9%

Length

2025-09-13T18:30:59.801492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-13T18:31:00.035959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
cash 35022
52.2%
upi 25881
38.6%
card 3064
 
4.6%
credit 2435
 
3.6%
debit 629
 
0.9%

Most occurring characters

ValueCountFrequency (%)
C 40521
16.2%
a 38086
15.2%
s 35022
14.0%
h 35022
14.0%
U 25881
10.3%
P 25881
10.3%
I 25881
10.3%
r 5499
 
2.2%
d 5499
 
2.2%
e 3064
 
1.2%
Other values (5) 10450
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 128949
51.4%
Uppercase Letter 118793
47.4%
Space Separator 3064
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 38086
29.5%
s 35022
27.2%
h 35022
27.2%
r 5499
 
4.3%
d 5499
 
4.3%
e 3064
 
2.4%
i 3064
 
2.4%
t 3064
 
2.4%
b 629
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
C 40521
34.1%
U 25881
21.8%
P 25881
21.8%
I 25881
21.8%
D 629
 
0.5%
Space Separator
ValueCountFrequency (%)
3064
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 247742
98.8%
Common 3064
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 40521
16.4%
a 38086
15.4%
s 35022
14.1%
h 35022
14.1%
U 25881
10.4%
P 25881
10.4%
I 25881
10.4%
r 5499
 
2.2%
d 5499
 
2.2%
e 3064
 
1.2%
Other values (4) 7386
 
3.0%
Common
ValueCountFrequency (%)
3064
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 250806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 40521
16.2%
a 38086
15.2%
s 35022
14.0%
h 35022
14.0%
U 25881
10.3%
P 25881
10.3%
I 25881
10.3%
r 5499
 
2.2%
d 5499
 
2.2%
e 3064
 
1.2%
Other values (5) 10450
 
4.2%

Ride_Distance
Real number (ℝ)

High correlation  Zeros 

Distinct50
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.189927
Minimum0
Maximum49
Zeros39057
Zeros (%)37.9%
Negative0
Negative (%)0.0%
Memory size805.0 KiB
2025-09-13T18:31:00.272961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8
Q326
95-th percentile45
Maximum49
Range49
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.77627
Coefficient of variation (CV)1.1117936
Kurtosis-0.81346365
Mean14.189927
Median Absolute Deviation (MAD)8
Skewness0.76059922
Sum1461903
Variance248.89068
MonotonicityNot monotonic
2025-09-13T18:31:00.545635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 39057
37.9%
10 1684
 
1.6%
14 1657
 
1.6%
1 1654
 
1.6%
16 1624
 
1.6%
4 1618
 
1.6%
5 1601
 
1.6%
18 1598
 
1.6%
15 1597
 
1.6%
17 1596
 
1.5%
Other values (40) 49338
47.9%
ValueCountFrequency (%)
0 39057
37.9%
1 1654
 
1.6%
2 1595
 
1.5%
3 1574
 
1.5%
4 1618
 
1.6%
5 1601
 
1.6%
6 1514
 
1.5%
7 1587
 
1.5%
8 1573
 
1.5%
9 1594
 
1.5%
ValueCountFrequency (%)
49 1109
1.1%
48 1036
1.0%
47 1133
1.1%
46 1126
1.1%
45 1163
1.1%
44 1097
1.1%
43 1120
1.1%
42 1159
1.1%
41 1101
1.1%
40 1074
1.0%

Driver_Ratings
Real number (ℝ)

High correlation 

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9946297
Minimum3
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.0 KiB
2025-09-13T18:31:00.781299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3.2
Q13.8
median3.99
Q34.2
95-th percentile4.8
Maximum5
Range2
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.45454034
Coefficient of variation (CV)0.11378785
Kurtosis-0.072567932
Mean3.9946297
Median Absolute Deviation (MAD)0.19
Skewness0.021121088
Sum411542.73
Variance0.20660692
MonotonicityNot monotonic
2025-09-13T18:31:01.082250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3.99 39057
37.9%
3.8 3313
 
3.2%
3.9 3274
 
3.2%
3.1 3273
 
3.2%
4.2 3259
 
3.2%
3.3 3249
 
3.2%
3.5 3235
 
3.1%
4.7 3232
 
3.1%
4.1 3231
 
3.1%
4.5 3214
 
3.1%
Other values (12) 34687
33.7%
ValueCountFrequency (%)
3 1560
1.5%
3.1 3273
3.2%
3.2 3161
3.1%
3.3 3249
3.2%
3.4 3130
3.0%
3.5 3235
3.1%
3.6 3124
3.0%
3.7 3208
3.1%
3.8 3313
3.2%
3.9 3274
3.2%
ValueCountFrequency (%)
5 1531
1.5%
4.9 3105
3.0%
4.8 3176
3.1%
4.7 3232
3.1%
4.6 3183
3.1%
4.5 3214
3.1%
4.4 3185
3.1%
4.3 3164
3.1%
4.2 3259
3.2%
4.1 3231
3.1%

Customer_Rating
Real number (ℝ)

High correlation  Missing 

Distinct21
Distinct (%)< 0.1%
Missing39057
Missing (%)37.9%
Infinite0
Infinite (%)0.0%
Mean3.9983132
Minimum3
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.0 KiB
2025-09-13T18:31:01.338934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3.1
Q13.5
median4
Q34.5
95-th percentile4.9
Maximum5
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.57895737
Coefficient of variation (CV)0.1448004
Kurtosis-1.1864231
Mean3.9983132
Median Absolute Deviation (MAD)0.5
Skewness0.0066774677
Sum255760.1
Variance0.33519163
MonotonicityNot monotonic
2025-09-13T18:31:01.578357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
4.9 3289
 
3.2%
3.9 3286
 
3.2%
3.5 3275
 
3.2%
3.7 3264
 
3.2%
4 3259
 
3.2%
3.2 3222
 
3.1%
4.8 3216
 
3.1%
4.4 3210
 
3.1%
3.4 3210
 
3.1%
3.3 3194
 
3.1%
Other values (11) 31542
30.6%
(Missing) 39057
37.9%
ValueCountFrequency (%)
3 1651
1.6%
3.1 3130
3.0%
3.2 3222
3.1%
3.3 3194
3.1%
3.4 3210
3.1%
3.5 3275
3.2%
3.6 3168
3.1%
3.7 3264
3.2%
3.8 3102
3.0%
3.9 3286
3.2%
ValueCountFrequency (%)
5 1566
1.5%
4.9 3289
3.2%
4.8 3216
3.1%
4.7 3126
3.0%
4.6 3129
3.0%
4.5 3173
3.1%
4.4 3210
3.1%
4.3 3152
3.1%
4.2 3173
3.1%
4.1 3172
3.1%

Interactions

2025-09-13T18:30:47.830261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:29.161782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:30.807011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:35.341114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:40.247604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:45.005264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:48.022202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:29.379251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:31.242757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:36.096205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:40.988661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:45.829115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:48.237898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:29.677460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:31.837181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:36.893315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:41.801635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:46.681505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:48.454596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:29.908021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:32.521987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:37.692188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:42.572924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:47.194950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:48.648077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:30.174411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:33.211510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:38.468396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:43.327374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:47.390694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:48.871935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:30.515493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:34.436280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:39.386181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:44.159545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T18:30:47.614341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-09-13T18:31:01.765259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Booking_StatusBooking_ValueC_TATCanceled_Rides_by_CustomerCanceled_Rides_by_DriverCustomer_RatingDriver_RatingsDrop_LocationIncomplete_RidesIncomplete_Rides_ReasonPayment_MethodPickup_LocationRide_DistanceV_TATVehicle_Type
Booking_Status1.0000.0001.0001.0001.0001.0000.5070.0011.0001.0001.0000.0090.5071.0000.002
Booking_Value0.0001.000-0.0000.0000.0200.0070.0000.0060.0120.0300.0000.000-0.001-0.0050.005
C_TAT1.000-0.0001.0000.0000.0000.001-0.0030.0070.0000.0410.0000.009-0.0000.0000.007
Canceled_Rides_by_Customer1.0000.0000.0001.0000.0000.0001.0000.0220.0000.0000.0000.0141.0000.0000.000
Canceled_Rides_by_Driver1.0000.0200.0000.0001.0000.0001.0000.0000.0000.0000.0000.0231.0000.0000.000
Customer_Rating1.0000.0070.0010.0000.0001.000-0.0030.0000.0060.0000.0070.0110.0100.0010.006
Driver_Ratings0.5070.000-0.0031.0001.000-0.0031.0000.0040.0000.0000.0080.0040.031-0.0020.005
Drop_Location0.0010.0060.0070.0220.0000.0000.0041.0000.0000.0000.0070.0040.0070.0020.000
Incomplete_Rides1.0000.0120.0000.0000.0000.0060.0000.0001.0001.0000.0000.0130.0080.0090.003
Incomplete_Rides_Reason1.0000.0300.0410.0000.0000.0000.0000.0001.0001.0000.0000.0000.0130.0000.000
Payment_Method1.0000.0000.0000.0000.0000.0070.0080.0070.0000.0001.0000.0040.0000.0060.000
Pickup_Location0.0090.0000.0090.0140.0230.0110.0040.0040.0130.0000.0041.0000.0030.0070.003
Ride_Distance0.507-0.001-0.0001.0001.0000.0100.0310.0070.0080.0130.0000.0031.000-0.0110.132
V_TAT1.000-0.0050.0000.0000.0000.001-0.0020.0020.0090.0000.0060.007-0.0111.0000.003
Vehicle_Type0.0020.0050.0070.0000.0000.0060.0050.0000.0030.0000.0000.0030.1320.0031.000

Missing values

2025-09-13T18:30:49.308544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-13T18:30:50.015607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-09-13T18:30:50.836908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DateTimeBooking_IDBooking_StatusCustomer_IDVehicle_TypePickup_LocationDrop_LocationV_TATC_TATCanceled_Rides_by_CustomerCanceled_Rides_by_DriverIncomplete_RidesIncomplete_Rides_ReasonBooking_ValuePayment_MethodRide_DistanceDriver_RatingsCustomer_Rating
026-07-2024 14:0014:00:00CNR7153255142Canceled by DriverCID713523Prime SedanTumkur RoadRT NagarNaNNaNNaNPersonal & Car related issueNaNNaN444NaN03.99NaN
125-07-2024 22:2022:20:00CNR2940424040SuccessCID225428BikeMagadi RoadVarthur203.030.0NaNNaNNoNaN158Cash134.104.0
230-07-2024 19:5919:59:00CNR2982357879SuccessCID270156Prime SUVSahakar NagarVarthur238.0130.0NaNNaNNoNaN386UPI404.204.8
322-07-2024 03:1503:15:00CNR2395710036Canceled by CustomerCID581320eBikeHSR LayoutVijayanagarNaNNaNDriver is not moving towards pickup locationNaNNaNNaN384NaN03.99NaN
402-07-2024 09:0209:02:00CNR1797421769SuccessCID939555MiniRajajinagarChamarajpet252.080.0NaNNaNNoNaN822Credit Card454.003.0
513-07-2024 04:4204:42:00CNR8787177882SuccessCID802429MiniKadugodiVijayanagar231.090.0NaNNaNNoNaN173UPI413.404.6
623-07-2024 09:5109:51:00CNR3612067560SuccessCID476071BikeTumkur RoadWhitefield133.040.0NaNNaNNoNaN140Cash493.204.5
711-07-2024 11:1211:12:00CNR5374902489Canceled by DriverCID735691Prime PlusBannerghatta RoadSarjapur RoadNaNNaNNaNPersonal & Car related issueNaNNaN344NaN03.99NaN
801-07-2024 19:1919:19:00CNR5030602354Driver Not FoundCID999840MiniChamarajpetPeenyaNaNNaNNaNNaNNaNNaN839NaN03.99NaN
918-07-2024 01:3101:31:00CNR6328453219Canceled by DriverCID907133AutoRT NagarVarthurNaNNaNNaNPersonal & Car related issueNaNNaN893NaN03.99NaN
DateTimeBooking_IDBooking_StatusCustomer_IDVehicle_TypePickup_LocationDrop_LocationV_TATC_TATCanceled_Rides_by_CustomerCanceled_Rides_by_DriverIncomplete_RidesIncomplete_Rides_ReasonBooking_ValuePayment_MethodRide_DistanceDriver_RatingsCustomer_Rating
10301431-07-2024 17:1117:11:00CNR8606968614Driver Not FoundCID259664BikeBanashankariYelahankaNaNNaNNaNNaNNaNNaN545NaN03.99NaN
10301531-07-2024 17:4317:43:00CNR1504131542Canceled by DriverCID663450Prime PlusBellandurHulimavuNaNNaNNaNCustomer related issueNaNNaN461NaN03.99NaN
10301631-07-2024 09:2009:20:00CNR3395373353Driver Not FoundCID355268Prime SedanMalleshwaramTumkur RoadNaNNaNNaNNaNNaNNaN1671NaN03.99NaN
10301731-07-2024 19:3419:34:00CNR4870774895SuccessCID266336Prime SedanMarathahalliCox Town189.030.0NaNNaNNoNaN280UPI383.404.1
10301831-07-2024 03:0003:00:00CNR9738039746SuccessCID922711eBikeChickpetRT Nagar42.0135.0NaNNaNNoNaN310UPI73.803.3
10301931-07-2024 09:0609:06:00CNR9488489435SuccessCID371654Prime PlusRichmond TownVarthur245.035.0NaNNaNNoNaN111Cash413.603.8
10302031-07-2024 15:1215:12:00CNR3151743100SuccessCID334158AutoVijayanagarRichmond Town84.0145.0NaNNaNNoNaN1097UPI174.303.3
10302131-07-2024 13:5913:59:00CNR1286151233SuccessCID113188Prime SUVBannerghatta RoadJP Nagar35.075.0NaNNaNNoNaN2201Cash373.603.2
10302231-07-2024 14:5614:56:00CNR2027162035SuccessCID118301eBikeIndiranagarMagadi Road210.0140.0NaNNaNNoNaN267UPI473.403.1
10302331-07-2024 13:5713:57:00CNR9770709721SuccessCID217959AutoUlsoorHennur175.0125.0NaNNaNNoNaN462UPI34.804.4